Correlations

Running correlations for Gui

Setup

Loading packages

list_of_packages <- c("tidyverse", "readxl", "here", "correlation", "DT")
new_packages <- list_of_packages[!(list_of_packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.1.0     ✓ dplyr   1.0.4
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(readxl)
library(here)

## here() starts at /home/francisko/coding/r/correlacoes-gui

library(correlation)
library(DT)

Loading data

data_path <- 
  here::here("data")


files <- 
  dir(path = data_path,
      pattern = "*.xls|*.xlsx")

adsorbed_particles <-
files %>% 
  purrr::map(~ readxl::read_excel(file.path(data_path, .))) %>%
  purrr::set_names(nm = c("particle_count",
                          "macro",
                          "micro",
                          "wetability",
                          "celular_perimeter",
                          "ui"
                          ))

Standardising the sheets

adsorbed_particles %>% 
  purrr::pluck("particle_count") %>% 
  dplyr::rename(sp = Sp,
                face = Face,
                frag = Frag,
                site = Site) %>% 
  dplyr::mutate(
    site = dplyr::case_when(
    site == "BR" ~ 1,
    site == "CA" ~ 2,
    site == "VZ" ~ 3,
    site == "CN" ~ 4,
    site == "REF" ~ 5),
    `<2.5` = as.numeric(`<2.5`),
    `2.5-10` = as.numeric(`2.5-10`),
    `10-100` = as.numeric(`10-100`),
    total = `<2.5` + `2.5-10` + `10-100`) -> adsorbed_particles$particle_count

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

adsorbed_particles %>%
  purrr::pluck("particle_count") %>%
  dplyr::group_by(site, sp, Rep, face) %>%
  dplyr::summarise(
    `<2.5` = mean(`<2.5`),
    `2.5-10` = mean(`2.5-10`),
    `10-100` = mean(`10-100`),
    total = mean(total)
  ) %>%
  dplyr::ungroup() -> adsorbed_particles$particle_count_micro

## `summarise()` has grouped output by 'site', 'sp', 'Rep'. You can override using the `.groups` argument.

adsorbed_particles %>% 
  purrr::pluck("micro") %>% 
  dplyr::rename(ind = rep) -> adsorbed_particles$micro

adsorbed_particles %>% 
  purrr::pluck("macro") %>% 
  dplyr::select(-Rq) -> adsorbed_particles$macro

adsorbed_particles %>% 
  purrr::pluck("micro") %>% 
  dplyr::select(-Sq) -> adsorbed_particles$micro

Combining sheets

Combining microroughness sheets

adsorbed_particles %>% 
  purrr::keep(stringr::str_detect(names(.), pattern = "micro")) %>% 
  purrr::reduce(left_join) %>% 
  dplyr::rename(rep = Rep) -> micro_combined

## Joining, by = c("sp", "face")

Correcting microroughness sheet

I forgot to change sp, site, face and rep to factors so I’ll do it now:

common_vars <- c("sp", "site", "face", "rep")

micro_combined %>%
  dplyr::mutate(across(.cols = all_of(common_vars), .fns = forcats::as_factor)) -> micro_combined

Combining all vars but microroughness

adsorbed_particles %>% 
  purrr::discard(stringr::str_detect(names(.), pattern = "micro")) %>% 
  purrr::reduce(left_join) %>% 
  dplyr::rename(rep = Rep) -> all_but_micro_combined

## Joining, by = c("sp", "face", "frag")

## Joining, by = c("sp", "face", "frag", "ind")
## Joining, by = c("sp", "face", "frag", "ind")
## Joining, by = c("sp", "face", "frag", "ind")

Correcting all vars but microroughness sheet

I forgot to change sp, site, face and rep to factors so I’ll do it now:

all_but_micro_combined %>%
  dplyr::mutate(across(.cols = all_of(common_vars), .fns = forcats::as_factor)) -> all_but_micro_combined

Making sheets available

Microroughness

micro_combined %>%
  DT::datatable(
    extensions = 'Buttons',
    options = list(
      dom = 'Blfrtip',
      buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
      lengthMenu = list(c(10, 25, 50, -1),
                        c(10, 25, 50, "All"))
    )
  )

all_but_micro_combined %>%
  DT::datatable(
    extensions = 'Buttons',
    options = list(
      dom = 'Blfrtip',
      buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
      lengthMenu = list(c(10, 25, 50, -1),
                        c(10, 25, 50, "All"))
    )
  )

All but Microuroughness

Analyses

Defining functions I used

Since there’s load of repetition in these analyses, I defined functions to make maintaining the code easier.

The following function calculates the pearson correlation between leaf_char and part_size for a given dataset (dat).

calculates_correlation <- function(dat, leaf_char, part_size) {

  common_vars <- c("sp", "site", "face", "rep")
  
  dat %>% 
    select(all_of(common_vars), {{leaf_char}}, {{part_size}}) %>% 
    group_by(site, sp, face, rep)  %>%
    summarise(mean({{leaf_char}}), mean({{part_size}})) %>% 
    ungroup() %>% 
    correlation()
}

plots_scatterplot <- function(dat, leaf_char, part_size) {

  common_vars <- c("sp", "site", "face", "rep")
  
  dat %>% 
    select(all_of(common_vars), {{leaf_char}}, {{part_size}}) %>% 
    group_by(site, sp, face, rep)  %>%
    summarise(mean_char = mean({{leaf_char}}), mean_part = mean({{part_size}})) %>% 
    ggplot2::ggplot(., aes(x = mean_char, y = mean_part)) +
    ggplot2::geom_point(alpha = 0.7) +
    ggplot2::geom_smooth(method = "lm",
                         se = F)
}

Microroughness

< 2.5

Correlation

calculates_correlation(micro_combined, Sa, `<2.5`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |   Parameter2 |     r |         95% CI | t(52) |       p
## --------------------------------------------------------------------
## mean(Sa)   | mean(`<2.5`) | -0.36 | [-0.57, -0.10] | -2.77 | 0.008**
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(micro_combined, Sa, `<2.5`) +
  labs(y = "Number of adsorbed particles (<2.5)",
       x = "Sa")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

2.5 - 10

Correlation

calculates_correlation(micro_combined, Sa, `2.5-10`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |     Parameter2 |     r |         95% CI | t(52) |         p
## ------------------------------------------------------------------------
## mean(Sa)   | mean(`2.5-10`) | -0.44 | [-0.63, -0.19] | -3.50 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(micro_combined, Sa, `2.5-10`) +
  labs(
    x = "Sa",
    y = "Number of adsorbed particles (2.5 - 10)"
  )

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

10 - 100

Correlation

calculates_correlation(micro_combined, Sa, `10-100`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |     Parameter2 |     r |         95% CI | t(55) |      p
## ---------------------------------------------------------------------
## mean(Sa)   | mean(`10-100`) | -0.33 | [-0.55, -0.08] | -2.61 | 0.012*
## 
## p-value adjustment method: Holm (1979)
## Observations: 57

Plot

plots_scatterplot(micro_combined, Sa, `10-100`) +
  labs(
    x = "Sa",
    y = "Number of adsorbed particles (10-100)"
  )

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 3 rows containing non-finite values (stat_smooth).

## Warning: Removed 3 rows containing missing values (geom_point).

Total

Correlation

calculates_correlation(micro_combined, Sa, total)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |  Parameter2 |     r |         95% CI | t(52) |       p
## -------------------------------------------------------------------
## mean(Sa)   | mean(total) | -0.42 | [-0.62, -0.17] | -3.30 | 0.002**
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(micro_combined, Sa, total) +
  labs(
    x = "Sa",
    y = "Total number of Adsorbed particles"
  )

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

Macroroughness

<2.5

Correlation

calculates_correlation(all_but_micro_combined, Ra, `<2.5`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |   Parameter2 |     r |         95% CI | t(52) |         p
## ----------------------------------------------------------------------
## mean(Ra)   | mean(`<2.5`) | -0.79 | [-0.88, -0.67] | -9.44 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, Ra, `<2.5`) + 
  labs(y = "Number of adsorbed particles (<2.5)",
       x = "Ra")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

2.5 - 10

Correlation

calculates_correlation(all_but_micro_combined, Ra, `2.5-10`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |     Parameter2 |     r |         95% CI |  t(52) |         p
## -------------------------------------------------------------------------
## mean(Ra)   | mean(`2.5-10`) | -0.82 | [-0.89, -0.70] | -10.26 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, Ra, `2.5-10`) +
  labs(y = "Number of adsorbed particles (2.5 - 10)",
       x = "Ra")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

10 - 100

Correlation

calculates_correlation(all_but_micro_combined, Ra, `10-100`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |     Parameter2 |     r |         95% CI | t(55) |         p
## ------------------------------------------------------------------------
## mean(Ra)   | mean(`10-100`) | -0.62 | [-0.76, -0.43] | -5.84 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 57

Plot

plots_scatterplot(all_but_micro_combined, Ra, `10-100`) +
  labs(y = "Number of adsorbed particles (10 - 100)",
       x = "Ra")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 3 rows containing non-finite values (stat_smooth).

## Warning: Removed 3 rows containing missing values (geom_point).

Total

Correlation

calculates_correlation(all_but_micro_combined, Ra, total) 

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |  Parameter2 |     r |         95% CI |  t(52) |         p
## ----------------------------------------------------------------------
## mean(Ra)   | mean(total) | -0.83 | [-0.90, -0.72] | -10.54 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, Ra, total) +
  labs(y = "Total number of adsorbed particles",
       x = "Ra")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

Wetability

<2.5

Correlation

calculates_correlation(all_but_micro_combined, Angle, `<2.5`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1  |   Parameter2 |     r |         95% CI | t(52) |       p
## ---------------------------------------------------------------------
## mean(Angle) | mean(`<2.5`) | -0.37 | [-0.58, -0.11] | -2.87 | 0.006**
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, Angle, `<2.5`) +
  labs(y = "Number of adsorbed particles (<2.5)",
       x = "Angle")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

2.5 - 10

Correlation

calculates_correlation(all_but_micro_combined, Angle, `2.5-10`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1  |     Parameter2 |     r |         95% CI | t(52) |         p
## -------------------------------------------------------------------------
## mean(Angle) | mean(`2.5-10`) | -0.46 | [-0.65, -0.22] | -3.71 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, Angle, `2.5-10`) +
  labs(y = "Number of adsorbed particles (2.5 - 10)",
       x = "Angle")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

10 - 100

Correlation

calculates_correlation(all_but_micro_combined, Angle, `10-100`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1  |     Parameter2 |     r |         95% CI | t(55) |      p
## ----------------------------------------------------------------------
## mean(Angle) | mean(`10-100`) | -0.33 | [-0.55, -0.08] | -2.61 | 0.012*
## 
## p-value adjustment method: Holm (1979)
## Observations: 57

Plot

plots_scatterplot(all_but_micro_combined, Angle, `10-100`) +
  labs(y = "Number of adsorbed particles (10 - 100)",
       x = "Angle")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 3 rows containing non-finite values (stat_smooth).

## Warning: Removed 3 rows containing missing values (geom_point).

Total

Correlation

calculates_correlation(all_but_micro_combined, Angle, total)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1  |  Parameter2 |     r |         95% CI | t(52) |       p
## --------------------------------------------------------------------
## mean(Angle) | mean(total) | -0.43 | [-0.63, -0.19] | -3.46 | 0.001**
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

plots_scatterplot(all_but_micro_combined, Angle, total) +
  labs(y = "Total number of adsorbed particles",
       x = "Angle")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

Perimeter

<2.5

Correlation

calculates_correlation(all_but_micro_combined, Perimeter, `<2.5`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1      |   Parameter2 |     r |         95% CI | t(52) |      p
## ------------------------------------------------------------------------
## mean(Perimeter) | mean(`<2.5`) | -0.30 | [-0.52, -0.03] | -2.26 | 0.028*
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, Perimeter, `<2.5`) +
  labs(y = "Number of adsorbed particles (<2.5)",
       x = "Perimeter")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

2.5 - 10

Correlation

calculates_correlation(all_but_micro_combined, Perimeter, `2.5-10`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1      |     Parameter2 |     r |         95% CI | t(52) |       p
## ---------------------------------------------------------------------------
## mean(Perimeter) | mean(`2.5-10`) | -0.38 | [-0.59, -0.13] | -2.99 | 0.004**
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, Perimeter, `2.5-10`) +
  labs(y = "Number of adsorbed particles (2.5 - 10)",
       x = "Perimeter")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

10 - 100

Correlation

calculates_correlation(all_but_micro_combined, Perimeter, `10-100`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1      |     Parameter2 |     r |         95% CI | t(55) |      p
## --------------------------------------------------------------------------
## mean(Perimeter) | mean(`10-100`) | -0.29 | [-0.51, -0.03] | -2.23 | 0.030*
## 
## p-value adjustment method: Holm (1979)
## Observations: 57

Plot

plots_scatterplot(all_but_micro_combined, Perimeter, `10-100`) +
  labs(y = "Number of adsorbed particles (10 - 100)",
       x = "Perimeter")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 3 rows containing non-finite values (stat_smooth).

## Warning: Removed 3 rows containing missing values (geom_point).

Total

Correlation

calculates_correlation(all_but_micro_combined, Perimeter, total)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1      |  Parameter2 |     r |         95% CI | t(52) |       p
## ------------------------------------------------------------------------
## mean(Perimeter) | mean(total) | -0.36 | [-0.57, -0.10] | -2.77 | 0.008**
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, Perimeter, total) +
  labs(y = "Total number of adsorbed particles",
       x = "Perimeter")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

UI

<2.5

Correlation

calculates_correlation(all_but_micro_combined, UI, `<2.5`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |   Parameter2 |     r |         95% CI | t(52) |         p
## ----------------------------------------------------------------------
## mean(UI)   | mean(`<2.5`) | -0.44 | [-0.63, -0.19] | -3.49 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, UI, `<2.5`) +
  labs(y = "Number of adsorbed particles (<2.5)",
       x = "UI")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

2.5 - 10

Correlation

calculates_correlation(all_but_micro_combined, UI, `2.5-10`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |     Parameter2 |     r |         95% CI | t(52) |         p
## ------------------------------------------------------------------------
## mean(UI)   | mean(`2.5-10`) | -0.52 | [-0.69, -0.29] | -4.40 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, UI, `2.5-10`) +
  labs(y = "Number of adsorbed particles (2.5 - 10)",
       x = "UI")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).

10 - 100

Correlation

calculates_correlation(all_but_micro_combined, UI, `10-100`)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |     Parameter2 |     r |         95% CI | t(55) |       p
## ----------------------------------------------------------------------
## mean(UI)   | mean(`10-100`) | -0.38 | [-0.58, -0.13] | -3.03 | 0.004**
## 
## p-value adjustment method: Holm (1979)
## Observations: 57

Plot

plots_scatterplot(all_but_micro_combined, UI, `10-100`) +
  labs(y = "Number of adsorbed particles (10 - 100)",
       x = "UI")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 3 rows containing non-finite values (stat_smooth).

## Warning: Removed 3 rows containing missing values (geom_point).

Total

Correlation

calculates_correlation(all_but_micro_combined, UI, total)

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## # Correlation table (pearson-method)
## 
## Parameter1 |  Parameter2 |     r |         95% CI | t(52) |         p
## ---------------------------------------------------------------------
## mean(UI)   | mean(total) | -0.50 | [-0.68, -0.27] | -4.14 | < .001***
## 
## p-value adjustment method: Holm (1979)
## Observations: 54

Plot

plots_scatterplot(all_but_micro_combined, UI, total) +
  labs(y = "Total number of adsorbed particles",
       x = "UI")

## `summarise()` has grouped output by 'site', 'sp', 'face'. You can override using the `.groups` argument.

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 6 rows containing non-finite values (stat_smooth).

## Warning: Removed 6 rows containing missing values (geom_point).